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Data Modeling

Today, all corporate players recognize data’s significance and role in a company’s success. But data can only benefit you if it is organized, tracked, actionable, and harnessed properly—one excellent way to do all that is data modeling 

Data modeling is critical because it enables you to design, develop, deploy, and leverage high-quality data assets. Don’t consider it as mere documentation – data modeling goes beyond that. It impacts all your business decisions – from day-to-day operations to long-term business strategy.  

So, what is data modeling, its types, and its benefits? This article provides a comprehensive overview of data modeling, its techniques, types, and benefits.  

What is Data Modeling?  

Data modeling, sometimes called data structuring, refers to defining and analyzing the data a company generates, collects, and possesses. It also extends to examining the relationships data bits have with one another.   

As a process, it helps you understand and determine your data requirements. It also creates visual representations of data according to how your teams use it.  

But this process first begins with identifying your business needs and comprehending the data you possess. This is the conceptual modeling stage. You then define data elements, structures, and relationships, which is the logical modeling stage.   

Then, you use this information to build a scalable database management system that aligns with your company’s needs and objectives. This stage is known as the physical modeling stage.  

The end goal of data models is to empower you with comprehensive analyses and business insights using the data you already have.   

Let’s look at data modeling techniques.  

Data Modeling Techniques and Languages  

Data scientists have developed numerous data modeling techniques and languages. The idea behind modeling techniques is to find an approach that best represents a firm’s data. And the modeling language aids in explaining the relationship between various data elements.  

A company’s domain knowledge and customer needs determine which modeling technique and language it would go for. However, the most famous modeling technique is the Entity Relationship Model (ER model), and the most well-known language is the Unified Modeling Language (UML).  

Let’s discuss these both briefly.  

  • Entity Relationship Model – The ER model offers a high-level diagrammatic view of a company’s data and the relationships between various data elements (concepts, entities, objects, people, etc.). These diagrams use different symbols (diamonds, rectangles, ovals, etc.) and lines to demonstrate the interconnectedness of multiple entities, attributes, and relationships.  
  • Unified Modeling Language – The UML emerged as a standard means to explain the relationship between data entities in a data model for large software houses. However, it is now used for data modeling across various other industries. As a data modeling language, its utility derives from being a shared communication medium used by data scientists to discuss data models.  

But the ER model and UML are just some of the data modeling techniques or languages in use today. There are other modeling techniques, too, like the Hierarchical Model, Object-oriented Model, and Object-relational Model.  

Similarly, other data structuring languages include Information Engineering, IDEF1X, and Barker Notation.   

Types of Data Modeling  

There are essentially three types of data modeling that combine to transform data into valuable models. Think of this as a process of building a house.  

You convey the idea of your house (conceptual modeling) to an architect, who takes it and furnishes a blueprint to you (logical modeling). Lastly, you hire a contractor to build your house for you (physical modeling). However, remember that this creates the model, but the data is still to enter, just like the house still needs an occupant.  

1. Conceptual Data Modeling  

The aim of conceptual data modeling, also known as enterprise data modeling, is to determine business rules and streamline ideas. The leading players in this process are business stakeholders and data architects/modelers. Business stakeholders outline what they want from the data, while the data modelers determine how to organize it to fulfill the requirements.  

Conceptual data modeling requires the unambiguous use of terms and vocabulary. This is done to ensure that all stakeholders understand the requirements correctly. For example, “latest” can mean multiple things to different business stakeholders. Does it refer to yesterday’s figures or this morning’s?  

Hence, it is critical to define vocabulary terms in unambiguous terms so that every stakeholder knows what a term means. Moreover, the conceptual modeling phase includes defining data entities, their attributes, and their relationships.   

Similarly, all differences regarding a business concept or piece of information between different teams need to be resolved at this stage of the modeling process.  

2. Logical Data Modeling  

The logical data modeling phase serves as the bridge between the preceding (conceptual) and subsequent (physical) modeling phases. It takes the conceptual model and transforms it into a series of instructions to create the physical model.  

Logical modeling entails defining the data structure and relationships between data bits. An entire database plan is made that explains the connections between various entities using a diagram. You can consider it a manifesto detailing different organizational groups’ data needs and requirements into a holistic data management game plan.  

3. Physical Data Modeling  

This is the last stage of the data structuring process. It involves the physical aspects of database storage. These include determining what kind of data goes into which columns, which column belongs to which table, and how the tables are connected to each other.  

Thus, this stage entails defining table structures, codes of data relationships, and indexes. In this phase, you have to decide which Database Management System (DMS) best suits your data model’s needs.   

Benefits of Data Modeling  

Data modeling presents incredible benefits for organizations, particularly those that possess vast volumes of data. It enables you to document the data you own, how it connects with other business elements, how it can be used, and what policies you need to implement to keep it secure.  

Thus, data structuring is critical for data management, governance, and artificial intelligence 

Here are some of the primary benefits of data structuring.  

1. Improved Business Understanding  

Enterprise data models empower you with powerful insights about your business and its various operations. By discovering, documenting, and standardizing your data sources, you can identify avenues and opportunities for optimizations.   

These models offer an enterprise-wide vision to you, highlighting the kinds of data that drive business decisions and fuel growth. With such models, a large enterprise can streamline business processes.   

You need to have a reasonably good idea of how your data assets are related to each other before you can optimize your processes. Data structuring offers you the critical foundation for building your business functions.  

2. Improved Regulatory Compliance 

You identify data management principles during the modeling process. It enables you to comply with industry standards and government regulations. And since regulations are only expected to increase, it is only sensible to institute mechanisms like data structuring that help compliance.   

3. Collaboration and Knowledge Transfer 

Since data modeling is data documenting in principle, it benefits IT Jobs and non-IT teams. It enables disparate groups to use the same jargon and language while communicating with each other without any confusion.   

Thus, data modeling allows for efficient collaboration whereby different teams can align themselves to optimize business processes and utilize the modeled data effectively. This, in turn, will enable employees to self-service, empowering them to exploit the data assets to their fullest.  

4. Enhanced Application Quality 

Data structuring, or modeling, gives you a clear vision and view of how data can help overcome problems and fulfill business needs. It allows your developers to identify and address issues proactively, thus eliminating the chances of significant hiccups that may occur later.  

5. Excellent Business Intelligence 

Data models allow everyone in the company to see the bigger picture. It lets them identify critical challenges and opportunities and give suggestions accordingly. This is possible because data models empower employees with data literacy, capability, and accountability.  

Given that everyone in the company fully comprehends what the data represents, employees feel a certain sense of responsibility and accountability in ensuring that the company leverages it to the maximum.  

Conclusion  

This article only touches on the surface of data modeling, particularly its benefits. There are many more benefits linked to it.   

The importance of data structuring in a data-driven world cannot be overemphasized. It drives major business decisions, streamlines workflows, eliminates bottlenecks, enables collaboration, and improves compliance.  

Big data is the new oil – a phrase we often come across. It is true. But even oil needs to be refined before being put to use. So is the case with data.   

Model your data to leverage it properly. Xavor can help you with this process – just as we have serviced many Fortune 500 companies and startups.  

Drop a line today by contacting us at info@xavor.com.

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Muhammad Abbas

Muhammad Abbas is an anthropologist at heart and a Marketing Specialist by profession. He has worked with leading marketing agencies over the years. While he considers himself a history buff, his interests also extend to topics like politics, economics, social justice, climate change, and tech.